New Generation Computing, 24(2006)325-350
Ohmsha, Ltd. and Springer

Tutorial Series on Brain-Inspired Computing
Part 4: Reinforcement Learning: Machine Learning and Natural Learning

Shin ISHII and Wako YOSHIDA
Nara Institute of Science and Technology
8916-5 Takayama, Ikoma, Nara 630-0192, Japan
{ishii,wako-y}@is.naist.jp

Received 29 October 2005
Revised manuscript received 28 February 2006

Abstract

The theory of reinforcement learning (RL) was originally motivated by animal learning of sequential behavior, but has been developed and extended in the field of machine learning as an approach to Markov decision processes. Recently, a number of neuroscience studies have suggested a relationship between reward-related activities in the brain and functions necessary for RL. Regarding the history of RL, we introduce in this article the theory of RL and present two engineering applications. Then we discuss possible implementations in the brain.

Keywords:Reinforcement Learning, Temporal Difference, Actor-critic, Reward System, Dopamine.

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